| Autor: |
Tongsen Zhu, Xuan Jiao, Xingshuo Li, Xuening Yin, Yang Du, Shuye Ding, Weidong Xiao |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
e-Prime: Advances in Electrical Engineering, Electronics and Energy, Vol 6, Iss , Pp 100315- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2772-6711 |
| DOI: |
10.1016/j.prime.2023.100315 |
| Popis: |
The prediction of global horizontal irradiance has become an effective technique to address the intermittence issue of photovoltaic (PV) power generation. This article proposes a novel deep neural network(DNN), named Mixer Dual Path Network (Mixer-DPN), for promising solar forecasting. It shares common features of cloud images and maintains the flexibility to explore new features through dual-path architecture by combining the Mixer layer and Dual Path Network. Therefore, the proposed model can provide more accurate prediction results compared to the classical DNN-based predictors. Moreover, the proposed model shows a faster convergence speed and smaller model size, which makes it suitable for a practical global horizontal irradiance. The merits of the proposed model are verified by testing it with the data from National Renewable Energy Laboratory comparing it with other DNN-based prediction models. Studies have shown that the new model has achieved excellent results in MSE, MAE and other indicators, and the R2 prediction accuracy rate has increased by 14% compared with the baseline model. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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